Forecast of NO2 concentrations based on coupled air quality model simulations and monitoring data using machine learning method

HUANG Yong-xi; ZHU Yun; XIE Yang-hong; LI Hai-xian; ZHANG Zhi-cheng; LI Jie; LI Jin-ying; YUAN Ying-zhi

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China Environmental Science ›› 2023, Vol. 43 ›› Issue (12) : 6225-6234.

Forecast of NO₂ concentrations based on coupled air quality model simulations and monitoring data using machine learning method

HUANG Yong-xi¹, ZHU Yun¹, XIE Yang-hong², LI Hai-xian², ZHANG Zhi-cheng¹, LI Jie¹, LI Jin-ying¹, YUAN Ying-zhi¹

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Abstract

In this study, built upon the WRF-CMAQ air quality model simulations, a novel machine learning method based on simulations and observations (SOML) that integrating feedforward neural network (FNN) and long short-term Memory network (LSTM) through the Lasso method was developed for forecasting NO₂ concentrations, where LSTM was derived based on real-time pollutant and meteorological data. This innovative method was then applied to forecast the NO₂ concentrations for three consecutive days for ten air quality monitoring stations in Shunde, Foshan to evaluate the model performance. Our results show that: Compared to WRF-CMAQ and other individual models, SOML gave higher accuracy in the three-day forecast of NO₂ concentrations, with the mean absolute error (MAE) of first day at 4.99μg/m³, decreasing up to 66.18%; The accuracy of SOML predictions has significantly improved compared with that of WRF-CMAQ, indicating SOML’s suitable applicability to all seasons (MAE decreased by 42.18%, 42.89%, 61.04% and 50.91%, respectively), particularly in autumn and winter; and Compared with WRF-CMAQ, SOML appears to provide better forecasting accuracy of the spatial distribution as well as the NO₂ concentration levels at each station in Shunde.

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air quality monitoring / forecast model / machine learning / NO₂ concentration forecast / WRF-CMAQ model

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HUANG Yong-xi, ZHU Yun, XIE Yang-hong, LI Hai-xian, ZHANG Zhi-cheng, LI Jie, LI Jin-ying, YUAN Ying-zhi. Forecast of NO₂ concentrations based on coupled air quality model simulations and monitoring data using machine learning method[J]. China Environmental Science. 2023, 43(12): 6225-6234

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